A Classification-Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

The Coronavirus Disease 2019 (COVID-19) is wreaking havoc around the world, bring out that the enormous pressure on national health and medical staff systems. One of the most effective and critical steps in the fight against COVID-19, is to examine the patient's lungs based on the Chest X-ray and CT generated by radiation imaging. In this paper, five keras- related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an classification- detection approaches of COVID-19. Two benchmark methods SVM (Support Vector Machine), CNN (Conventional Neural Networks) are provided to compare with the classification-detection approaches based on the performance indicators, i.e., precision, recall, F1 scores, confusion matrix, classification accuracy and three types of AUC (Area Under Curve). The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84% and 75%, which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.